Overview

Dataset statistics

Number of variables32
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory165.0 B

Variable types

Numeric16
Categorical3
Boolean13

Alerts

credit_per_loan is highly overall correlated with debt_to_income and 2 other fieldsHigh correlation
debt_to_income is highly overall correlated with credit_per_loan and 3 other fieldsHigh correlation
dependents_to_income is highly overall correlated with num_dependentsHigh correlation
home_ownership_OWN is highly overall correlated with home_ownership_RENTHigh correlation
home_ownership_RENT is highly overall correlated with home_ownership_OWNHigh correlation
income is highly overall correlated with debt_to_income and 2 other fieldsHigh correlation
loan_amount is highly overall correlated with credit_per_loan and 2 other fieldsHigh correlation
loan_to_income is highly overall correlated with credit_per_loan and 3 other fieldsHigh correlation
marital_status_Married is highly overall correlated with marital_status_SingleHigh correlation
marital_status_Single is highly overall correlated with marital_status_MarriedHigh correlation
num_dependents is highly overall correlated with dependents_to_incomeHigh correlation
target_default_risk is highly overall correlated with incomeHigh correlation
recent_default is highly imbalanced (72.6%)Imbalance
home_ownership_OTHER is highly imbalanced (73.4%)Imbalance
education_Other is highly imbalanced (71.4%)Imbalance
education_PhD is highly imbalanced (73.0%)Imbalance
marital_status_Widowed is highly imbalanced (70.9%)Imbalance
num_dependents has 2984 (29.8%) zerosZeros
signup_dayofweek has 1454 (14.5%) zerosZeros
dependents_to_income has 2984 (29.8%) zerosZeros

Reproduction

Analysis started2026-01-06 10:16:05.660025
Analysis finished2026-01-06 10:18:13.618309
Duration2 minutes and 7.96 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.8616
Minimum18
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:13.995729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median46
Q360
95-th percentile72
Maximum74
Range56
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.457987
Coefficient of variation (CV)0.35886203
Kurtosis-1.1934533
Mean45.8616
Median Absolute Deviation (MAD)14
Skewness0.017032931
Sum458616
Variance270.86533
MonotonicityNot monotonic
2026-01-06T15:48:14.538212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54209
 
2.1%
73209
 
2.1%
53199
 
2.0%
36194
 
1.9%
34192
 
1.9%
48191
 
1.9%
46190
 
1.9%
25189
 
1.9%
21187
 
1.9%
58186
 
1.9%
Other values (47)8054
80.5%
ValueCountFrequency (%)
18165
1.7%
19177
1.8%
20186
1.9%
21187
1.9%
22177
1.8%
23166
1.7%
24174
1.7%
25189
1.9%
26185
1.8%
27168
1.7%
ValueCountFrequency (%)
74181
1.8%
73209
2.1%
72165
1.7%
71172
1.7%
70179
1.8%
69166
1.7%
68163
1.6%
67159
1.6%
66167
1.7%
65148
1.5%

income
Real number (ℝ)

High correlation 

Distinct8632
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57469.287
Minimum20001
Maximum140959.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:15.078654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile22247.85
Q131753.75
median47301.5
Q373914
95-th percentile138951
Maximum140959.88
Range120958.88
Interquartile range (IQR)42160.25

Descriptive statistics

Standard deviation33077.06
Coefficient of variation (CV)0.57556064
Kurtosis0.35263367
Mean57469.287
Median Absolute Deviation (MAD)18540
Skewness1.1099336
Sum5.7469287 × 108
Variance1.0940919 × 109
MonotonicityNot monotonic
2026-01-06T15:48:15.636199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140959.875479
 
4.8%
47301.5318
 
3.2%
207024
 
< 0.1%
212324
 
< 0.1%
309463
 
< 0.1%
310083
 
< 0.1%
235333
 
< 0.1%
220163
 
< 0.1%
239383
 
< 0.1%
252403
 
< 0.1%
Other values (8622)9177
91.8%
ValueCountFrequency (%)
200011
< 0.1%
200031
< 0.1%
200121
< 0.1%
200181
< 0.1%
200241
< 0.1%
200271
< 0.1%
200312
< 0.1%
200341
< 0.1%
200401
< 0.1%
200461
< 0.1%
ValueCountFrequency (%)
140959.875479
4.8%
1409511
 
< 0.1%
1409021
 
< 0.1%
1408671
 
< 0.1%
1406911
 
< 0.1%
1406901
 
< 0.1%
1406781
 
< 0.1%
1405041
 
< 0.1%
1404981
 
< 0.1%
1404671
 
< 0.1%

savings
Real number (ℝ)

Distinct6035
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4747.2793
Minimum0
Maximum15251
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:16.210271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile271
Q11530
median3499
Q36826
95-th percentile14803.05
Maximum15251
Range15251
Interquartile range (IQR)5296

Descriptive statistics

Standard deviation4143.2224
Coefficient of variation (CV)0.87275724
Kurtosis0.35315755
Mean4747.2793
Median Absolute Deviation (MAD)2349
Skewness1.1026289
Sum47472793
Variance17166292
MonotonicityNot monotonic
2026-01-06T15:48:16.781555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15251472
 
4.7%
3499313
 
3.1%
13088
 
0.1%
2757
 
0.1%
18036
 
0.1%
446
 
0.1%
32526
 
0.1%
9566
 
0.1%
466
 
0.1%
7236
 
0.1%
Other values (6025)9164
91.6%
ValueCountFrequency (%)
02
< 0.1%
13
< 0.1%
21
 
< 0.1%
32
< 0.1%
43
< 0.1%
54
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
82
< 0.1%
102
< 0.1%
ValueCountFrequency (%)
15251472
4.7%
152481
 
< 0.1%
152301
 
< 0.1%
152231
 
< 0.1%
151831
 
< 0.1%
151581
 
< 0.1%
151521
 
< 0.1%
151241
 
< 0.1%
151031
 
< 0.1%
150561
 
< 0.1%

monthly_expenses
Real number (ℝ)

Distinct3000
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.6226
Minimum200
Maximum4186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:17.350524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile711.95
Q11492
median2007
Q32540
95-th percentile3344.05
Maximum4186
Range3986
Interquartile range (IQR)1048

Descriptive statistics

Standard deviation786.38505
Coefficient of variation (CV)0.38995152
Kurtosis-0.10892491
Mean2016.6226
Median Absolute Deviation (MAD)523
Skewness0.1063218
Sum20166226
Variance618401.44
MonotonicityNot monotonic
2026-01-06T15:48:18.090553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2007331
 
3.3%
200124
 
1.2%
418670
 
0.7%
149514
 
0.1%
206413
 
0.1%
152813
 
0.1%
160012
 
0.1%
236112
 
0.1%
241011
 
0.1%
256811
 
0.1%
Other values (2990)9389
93.9%
ValueCountFrequency (%)
200124
1.2%
2021
 
< 0.1%
2041
 
< 0.1%
2091
 
< 0.1%
2221
 
< 0.1%
2231
 
< 0.1%
2251
 
< 0.1%
2271
 
< 0.1%
2302
 
< 0.1%
2322
 
< 0.1%
ValueCountFrequency (%)
418670
0.7%
41842
 
< 0.1%
41711
 
< 0.1%
41301
 
< 0.1%
41261
 
< 0.1%
41231
 
< 0.1%
41181
 
< 0.1%
41071
 
< 0.1%
40941
 
< 0.1%
40923
 
< 0.1%

num_dependents
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2142
Minimum0
Maximum7
Zeros2984
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:18.578409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1089821
Coefficient of variation (CV)0.91334386
Kurtosis0.98351171
Mean1.2142
Median Absolute Deviation (MAD)1
Skewness0.94154018
Sum12142
Variance1.2298413
MonotonicityNot monotonic
2026-01-06T15:48:18.934668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
13603
36.0%
02984
29.8%
22166
21.7%
3891
 
8.9%
4275
 
2.8%
557
 
0.6%
619
 
0.2%
75
 
0.1%
ValueCountFrequency (%)
02984
29.8%
13603
36.0%
22166
21.7%
3891
 
8.9%
4275
 
2.8%
557
 
0.6%
619
 
0.2%
75
 
0.1%
ValueCountFrequency (%)
75
 
0.1%
619
 
0.2%
557
 
0.6%
4275
 
2.8%
3891
 
8.9%
22166
21.7%
13603
36.0%
02984
29.8%

credit_score
Real number (ℝ)

Distinct9615
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.17551
Minimum459.16859
Maximum840.55874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:19.392389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum459.16859
5-th percentile538.05669
Q1604.17918
median649.80832
Q3695.50082
95-th percentile764.64952
Maximum840.55874
Range381.39015
Interquartile range (IQR)91.321637

Descriptive statistics

Standard deviation68.484462
Coefficient of variation (CV)0.10533227
Kurtosis-0.10014083
Mean650.17551
Median Absolute Deviation (MAD)45.689101
Skewness0.02627284
Sum6501755.1
Variance4690.1215
MonotonicityNot monotonic
2026-01-06T15:48:20.029700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
649.8083219326
 
3.3%
840.55873936
 
0.4%
459.168587926
 
0.3%
721.74373581
 
< 0.1%
572.71875371
 
< 0.1%
714.81302561
 
< 0.1%
611.35073941
 
< 0.1%
663.97555571
 
< 0.1%
605.07620411
 
< 0.1%
683.29196711
 
< 0.1%
Other values (9605)9605
96.0%
ValueCountFrequency (%)
459.168587926
0.3%
459.67693471
 
< 0.1%
460.05974491
 
< 0.1%
460.15609881
 
< 0.1%
460.39142861
 
< 0.1%
460.6564151
 
< 0.1%
461.13992931
 
< 0.1%
461.57098481
 
< 0.1%
462.45782231
 
< 0.1%
463.13372181
 
< 0.1%
ValueCountFrequency (%)
840.55873936
0.4%
840.40555921
 
< 0.1%
838.95733411
 
< 0.1%
838.88281331
 
< 0.1%
838.79965651
 
< 0.1%
837.19235181
 
< 0.1%
837.06354661
 
< 0.1%
837.05999851
 
< 0.1%
837.04445361
 
< 0.1%
835.74636251
 
< 0.1%

loan_amount
Real number (ℝ)

High correlation 

Distinct7911
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15549.856
Minimum1000
Maximum41846.625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:20.604578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q18508.5
median15174.5
Q321843.75
95-th percentile31818.15
Maximum41846.625
Range40846.625
Interquartile range (IQR)13335.25

Descriptive statistics

Standard deviation9348.6275
Coefficient of variation (CV)0.60120347
Kurtosis-0.31421745
Mean15549.856
Median Absolute Deviation (MAD)6669.5
Skewness0.37343863
Sum1.5549856 × 108
Variance87396837
MonotonicityNot monotonic
2026-01-06T15:48:21.142491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000740
 
7.4%
41846.62589
 
0.9%
129287
 
0.1%
100006
 
0.1%
146114
 
< 0.1%
200014
 
< 0.1%
196734
 
< 0.1%
212854
 
< 0.1%
187414
 
< 0.1%
90154
 
< 0.1%
Other values (7901)9134
91.3%
ValueCountFrequency (%)
1000740
7.4%
10041
 
< 0.1%
10051
 
< 0.1%
10131
 
< 0.1%
10211
 
< 0.1%
10241
 
< 0.1%
10291
 
< 0.1%
10351
 
< 0.1%
10491
 
< 0.1%
10601
 
< 0.1%
ValueCountFrequency (%)
41846.62589
0.9%
417461
 
< 0.1%
417161
 
< 0.1%
417011
 
< 0.1%
416771
 
< 0.1%
416311
 
< 0.1%
415441
 
< 0.1%
415161
 
< 0.1%
414251
 
< 0.1%
413211
 
< 0.1%

loan_term_months
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.642
Minimum12
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:21.527094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q136
median48
Q360
95-th percentile72
Maximum72
Range60
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.475134
Coefficient of variation (CV)0.33905469
Kurtosis-0.44393278
Mean45.642
Median Absolute Deviation (MAD)12
Skewness-0.19759142
Sum456420
Variance239.47978
MonotonicityNot monotonic
2026-01-06T15:48:21.854325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
482998
30.0%
362523
25.2%
602008
20.1%
721003
 
10.0%
24948
 
9.5%
12520
 
5.2%
ValueCountFrequency (%)
12520
 
5.2%
24948
 
9.5%
362523
25.2%
482998
30.0%
602008
20.1%
721003
 
10.0%
ValueCountFrequency (%)
721003
 
10.0%
602008
20.1%
482998
30.0%
362523
25.2%
24948
 
9.5%
12520
 
5.2%

employment_years
Real number (ℝ)

Distinct182
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.39701
Minimum0
Maximum21.5
Zeros44
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:22.346818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q12.7
median5.1
Q37.7
95-th percentile11.5
Maximum21.5
Range21.5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4136997
Coefficient of variation (CV)0.63251683
Kurtosis-0.14701843
Mean5.39701
Median Absolute Deviation (MAD)2.5
Skewness0.52890029
Sum53970.1
Variance11.653345
MonotonicityNot monotonic
2026-01-06T15:48:22.900006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.4124
 
1.2%
2.9121
 
1.2%
3.8119
 
1.2%
5.3119
 
1.2%
0.1118
 
1.2%
3.6118
 
1.2%
4.9116
 
1.2%
4.7116
 
1.2%
4.6114
 
1.1%
2.5114
 
1.1%
Other values (172)8821
88.2%
ValueCountFrequency (%)
044
 
0.4%
0.1118
1.2%
0.2101
1.0%
0.391
0.9%
0.478
0.8%
0.592
0.9%
0.683
0.8%
0.797
1.0%
0.889
0.9%
0.999
1.0%
ValueCountFrequency (%)
21.51
< 0.1%
19.51
< 0.1%
18.91
< 0.1%
18.41
< 0.1%
18.22
< 0.1%
17.91
< 0.1%
17.81
< 0.1%
17.71
< 0.1%
17.62
< 0.1%
17.51
< 0.1%

recent_default
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0.0
9530 
1.0
 
470

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09530
95.3%
1.0470
 
4.7%

Length

2026-01-06T15:48:23.682909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-06T15:48:23.985932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.09530
95.3%
1.0470
 
4.7%

Most occurring characters

ValueCountFrequency (%)
019530
65.1%
.10000
33.3%
1470
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
019530
65.1%
.10000
33.3%
1470
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
019530
65.1%
.10000
33.3%
1470
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
019530
65.1%
.10000
33.3%
1470
 
1.6%

has_credit_card
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1.0
6948 
0.0
3052 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.06948
69.5%
0.03052
30.5%

Length

2026-01-06T15:48:24.299176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-06T15:48:24.598598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.06948
69.5%
0.03052
30.5%

Most occurring characters

ValueCountFrequency (%)
013052
43.5%
.10000
33.3%
16948
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013052
43.5%
.10000
33.3%
16948
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013052
43.5%
.10000
33.3%
16948
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013052
43.5%
.10000
33.3%
16948
23.2%

signup_dayofweek
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0119
Minimum0
Maximum6
Zeros1454
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:24.847061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.003986
Coefficient of variation (CV)0.6653561
Kurtosis-1.2553989
Mean3.0119
Median Absolute Deviation (MAD)2
Skewness-0.017084941
Sum30119
Variance4.01596
MonotonicityNot monotonic
2026-01-06T15:48:25.197056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
51488
14.9%
21461
14.6%
01454
14.5%
41430
14.3%
61420
14.2%
31385
13.9%
11362
13.6%
ValueCountFrequency (%)
01454
14.5%
11362
13.6%
21461
14.6%
31385
13.9%
41430
14.3%
51488
14.9%
61420
14.2%
ValueCountFrequency (%)
61420
14.2%
51488
14.9%
41430
14.3%
31385
13.9%
21461
14.6%
11362
13.6%
01454
14.5%

debt_to_income
Real number (ℝ)

High correlation 

Distinct1030
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3511229
Minimum0.004
Maximum1.072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:25.701870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.024
Q10.132
median0.275
Q30.508
95-th percentile0.96
Maximum1.072
Range1.068
Interquartile range (IQR)0.376

Descriptive statistics

Standard deviation0.28053519
Coefficient of variation (CV)0.79896581
Kurtosis0.098464982
Mean0.3511229
Median Absolute Deviation (MAD)0.17
Skewness0.9517573
Sum3511.229
Variance0.078699994
MonotonicityNot monotonic
2026-01-06T15:48:26.314737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.072318
 
3.2%
0.02440
 
0.4%
0.01740
 
0.4%
0.02837
 
0.4%
0.01136
 
0.4%
0.01234
 
0.3%
0.02633
 
0.3%
0.01832
 
0.3%
0.1331
 
0.3%
0.0231
 
0.3%
Other values (1020)9368
93.7%
ValueCountFrequency (%)
0.0045
 
0.1%
0.0054
 
< 0.1%
0.00616
0.2%
0.00722
0.2%
0.00823
0.2%
0.00922
0.2%
0.0123
0.2%
0.01136
0.4%
0.01234
0.3%
0.01327
0.3%
ValueCountFrequency (%)
1.072318
3.2%
1.0712
 
< 0.1%
1.0681
 
< 0.1%
1.0662
 
< 0.1%
1.0631
 
< 0.1%
1.0621
 
< 0.1%
1.0611
 
< 0.1%
1.062
 
< 0.1%
1.0582
 
< 0.1%
1.0562
 
< 0.1%

sin_age
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.10038733
Minimum-0.99992326
Maximum0.97384763
Zeros0
Zeros (%)0.0%
Negative5473
Negative (%)54.7%
Memory size78.3 KiB
2026-01-06T15:48:26.984535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.99992326
5-th percentile-0.993691
Q1-0.7568025
median-0.15774569
Q30.51550137
95-th percentile0.90929743
Maximum0.97384763
Range1.9737709
Interquartile range (IQR)1.2723039

Descriptive statistics

Standard deviation0.66742807
Coefficient of variation (CV)-6.6485288
Kurtosis-1.4372524
Mean-0.10038733
Median Absolute Deviation (MAD)0.65185905
Skewness0.13670034
Sum-1003.8733
Variance0.44546023
MonotonicityNot monotonic
2026-01-06T15:48:27.578480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7727644876209
 
2.1%
0.8504366206209
 
2.1%
-0.8322674422199
 
2.0%
-0.4425204433194
 
1.9%
-0.255541102192
 
1.9%
-0.9961646088191
 
1.9%
-0.9936910036190
 
1.9%
0.5984721441189
 
1.9%
0.8632093666187
 
1.9%
-0.4646021794186
 
1.9%
Other values (47)8054
80.5%
ValueCountFrequency (%)
-0.9999232576180
1.8%
-0.9961646088191
1.9%
-0.9936910036190
1.9%
-0.9824526126185
1.8%
-0.9775301177170
1.7%
-0.9589242747177
1.8%
-0.9516020739147
1.5%
-0.9258146823154
1.5%
-0.9161659367164
1.6%
-0.8834546557164
1.6%
ValueCountFrequency (%)
0.9738476309165
1.7%
0.9463000877177
1.8%
0.9092974268186
1.9%
0.8987080958181
1.8%
0.8632093666187
1.9%
0.8504366206209
2.1%
0.8084964038177
1.8%
0.7936678638165
1.7%
0.7457052122166
1.7%
0.7289690401172
1.7%

target_default_risk
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5132 
0
4868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Length

2026-01-06T15:48:28.275361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-06T15:48:28.589423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Most occurring characters

ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15132
51.3%
04868
48.7%

loan_to_income
Real number (ℝ)

High correlation 

Distinct9924
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3603494
Minimum0.0070941671
Maximum1.8806627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:28.961003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0070941671
5-th percentile0.023692617
Q10.13774766
median0.27824818
Q30.50676742
95-th percentile0.95988535
Maximum1.8806627
Range1.8735685
Interquartile range (IQR)0.36901976

Descriptive statistics

Standard deviation0.30169161
Coefficient of variation (CV)0.8372197
Kurtosis2.0450401
Mean0.3603494
Median Absolute Deviation (MAD)0.16752212
Skewness1.3563514
Sum3603.494
Variance0.091017829
MonotonicityNot monotonic
2026-01-06T15:48:29.652507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00709416708735
 
0.4%
0.0211405316826
 
0.3%
0.29686694987
 
0.1%
0.88465990173
 
< 0.1%
0.14313900932
 
< 0.1%
0.013702571972
 
< 0.1%
0.10974052
 
< 0.1%
0.041099831492
 
< 0.1%
0.20912185742
 
< 0.1%
0.032248701992
 
< 0.1%
Other values (9914)9917
99.2%
ValueCountFrequency (%)
0.00709416708735
0.4%
0.0070988443081
 
< 0.1%
0.0072431445961
 
< 0.1%
0.0073375646621
 
< 0.1%
0.0074213155021
 
< 0.1%
0.0074487895721
 
< 0.1%
0.0074645248461
 
< 0.1%
0.0074919835781
 
< 0.1%
0.0075562372961
 
< 0.1%
0.0076215445821
 
< 0.1%
ValueCountFrequency (%)
1.8806626671
< 0.1%
1.8522762481
< 0.1%
1.8357808731
< 0.1%
1.8315224531
< 0.1%
1.820871811
< 0.1%
1.8118559491
< 0.1%
1.7816193581
< 0.1%
1.7739889361
< 0.1%
1.7714356771
< 0.1%
1.7699522511
< 0.1%

dependents_to_income
Real number (ℝ)

High correlation  Zeros 

Distinct6307
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8103964 × 10-5
Minimum0
Maximum0.00029208451
Zeros2984
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:30.253578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.0814058 × 10-5
Q34.0709982 × 10-5
95-th percentile9.1431081 × 10-5
Maximum0.00029208451
Range0.00029208451
Interquartile range (IQR)4.0709982 × 10-5

Descriptive statistics

Standard deviation3.2022544 × 10-5
Coefficient of variation (CV)1.1394316
Kurtosis5.4997579
Mean2.8103964 × 10-5
Median Absolute Deviation (MAD)2.0814058 × 10-5
Skewness1.9167087
Sum0.28103964
Variance1.0254433 × 10-9
MonotonicityNot monotonic
2026-01-06T15:48:30.787844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02984
29.8%
7.094167087 × 10-6177
 
1.8%
1.418833417 × 10-5111
 
1.1%
2.114053168 × 10-5106
 
1.1%
4.228106337 × 10-556
 
0.6%
6.342159505 × 10-546
 
0.5%
2.128250126 × 10-537
 
0.4%
2.837666835 × 10-59
 
0.1%
8.456212674 × 10-56
 
0.1%
3.547083544 × 10-55
 
0.1%
Other values (6297)6463
64.6%
ValueCountFrequency (%)
02984
29.8%
7.094167087 × 10-6177
 
1.8%
7.094613769 × 10-61
 
< 0.1%
7.098844308 × 10-61
 
< 0.1%
7.117488381 × 10-61
 
< 0.1%
7.127786965 × 10-61
 
< 0.1%
7.142908164 × 10-61
 
< 0.1%
7.15778624 × 10-61
 
< 0.1%
7.194762213 × 10-61
 
< 0.1%
7.197403177 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.00029208450981
< 0.1%
0.00028427935181
< 0.1%
0.0002729009371
< 0.1%
0.00027257859351
< 0.1%
0.00026709401711
< 0.1%
0.00023432374171
< 0.1%
0.0002325491261
< 0.1%
0.00021728738431
< 0.1%
0.0002055836521
< 0.1%
0.0001968019681
< 0.1%

credit_per_loan
Real number (ℝ)

High correlation 

Distinct9964
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10564722
Minimum0.011573535
Maximum0.83971902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:31.344376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.011573535
5-th percentile0.019796413
Q10.029481527
median0.042977055
Q30.077430371
95-th percentile0.62517046
Maximum0.83971902
Range0.82814548
Interquartile range (IQR)0.047948843

Descriptive statistics

Standard deviation0.16919046
Coefficient of variation (CV)1.6014663
Kurtosis6.2453623
Mean0.10564722
Median Absolute Deviation (MAD)0.017089315
Skewness2.7332159
Sum1056.4722
Variance0.028625411
MonotonicityNot monotonic
2026-01-06T15:48:31.955418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.649159162726
 
0.3%
0.015527961795
 
0.1%
0.839719024
 
< 0.1%
0.027193183882
 
< 0.1%
0.4587098782
 
< 0.1%
0.067351608822
 
< 0.1%
0.046527876412
 
< 0.1%
0.46282644011
 
< 0.1%
0.042068913471
 
< 0.1%
0.029088381621
 
< 0.1%
Other values (9954)9954
99.5%
ValueCountFrequency (%)
0.011573535451
< 0.1%
0.011971693811
< 0.1%
0.012309727361
< 0.1%
0.012436821491
< 0.1%
0.012503245491
< 0.1%
0.012540136321
< 0.1%
0.012767207981
< 0.1%
0.012949647131
< 0.1%
0.013003054361
< 0.1%
0.013186499321
< 0.1%
ValueCountFrequency (%)
0.839719024
< 0.1%
0.83956599321
 
< 0.1%
0.82996758711
 
< 0.1%
0.82851695221
 
< 0.1%
0.82674472381
 
< 0.1%
0.82278567261
 
< 0.1%
0.81955720211
 
< 0.1%
0.81712966341
 
< 0.1%
0.81679900381
 
< 0.1%
0.81271879991
 
< 0.1%

account_age_days
Real number (ℝ)

Distinct1982
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1926.4608
Minimum928
Maximum2927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2026-01-06T15:48:32.652972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum928
5-th percentile1037
Q11431.75
median1919
Q32425
95-th percentile2827
Maximum2927
Range1999
Interquartile range (IQR)993.25

Descriptive statistics

Standard deviation575.41442
Coefficient of variation (CV)0.29868992
Kurtosis-1.1960711
Mean1926.4608
Median Absolute Deviation (MAD)496
Skewness0.016860433
Sum19264608
Variance331101.76
MonotonicityNot monotonic
2026-01-06T15:48:33.163688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
275514
 
0.1%
180513
 
0.1%
172813
 
0.1%
151413
 
0.1%
167912
 
0.1%
134012
 
0.1%
236512
 
0.1%
285612
 
0.1%
164512
 
0.1%
108912
 
0.1%
Other values (1972)9875
98.8%
ValueCountFrequency (%)
9286
0.1%
9294
 
< 0.1%
9304
 
< 0.1%
93110
0.1%
9323
 
< 0.1%
9333
 
< 0.1%
9344
 
< 0.1%
9356
0.1%
9365
0.1%
9377
0.1%
ValueCountFrequency (%)
29278
0.1%
29265
0.1%
29253
 
< 0.1%
29243
 
< 0.1%
29232
 
< 0.1%
29224
< 0.1%
29213
 
< 0.1%
29205
0.1%
29195
0.1%
29182
 
< 0.1%

home_ownership_OTHER
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
9548 
True
 
452
ValueCountFrequency (%)
False9548
95.5%
True452
 
4.5%
2026-01-06T15:48:33.518878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

home_ownership_OWN
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7474 
True
2526 
ValueCountFrequency (%)
False7474
74.7%
True2526
 
25.3%
2026-01-06T15:48:33.699587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

home_ownership_RENT
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
5476 
True
4524 
ValueCountFrequency (%)
False5476
54.8%
True4524
45.2%
2026-01-06T15:48:33.899688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

education_HS
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7454 
True
2546 
ValueCountFrequency (%)
False7454
74.5%
True2546
 
25.5%
2026-01-06T15:48:34.097465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
8038 
True
1962 
ValueCountFrequency (%)
False8038
80.4%
True1962
 
19.6%
2026-01-06T15:48:34.286117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

education_Other
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
9500 
True
 
500
ValueCountFrequency (%)
False9500
95.0%
True500
 
5.0%
2026-01-06T15:48:34.465517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

education_PhD
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
9538 
True
 
462
ValueCountFrequency (%)
False9538
95.4%
True462
 
4.6%
2026-01-06T15:48:34.630525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

marital_status_Married
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
5998 
True
4002 
ValueCountFrequency (%)
False5998
60.0%
True4002
40.0%
2026-01-06T15:48:34.795698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

marital_status_Single
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
5514 
True
4486 
ValueCountFrequency (%)
False5514
55.1%
True4486
44.9%
2026-01-06T15:48:34.999576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

marital_status_Widowed
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
9488 
True
 
512
ValueCountFrequency (%)
False9488
94.9%
True512
 
5.1%
2026-01-06T15:48:35.182092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

region_North
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7521 
True
2479 
ValueCountFrequency (%)
False7521
75.2%
True2479
 
24.8%
2026-01-06T15:48:35.356942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

region_South
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7477 
True
2523 
ValueCountFrequency (%)
False7477
74.8%
True2523
 
25.2%
2026-01-06T15:48:35.531689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

region_West
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7555 
True
2445 
ValueCountFrequency (%)
False7555
75.5%
True2445
 
24.4%
2026-01-06T15:48:35.710936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2026-01-06T15:48:03.258033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:15.989616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:22.524074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:28.937349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:36.136411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:43.578643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:50.121719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:57.386046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:04.546818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:12.388390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:18.849254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:25.589206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:32.301403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:41.294625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:49.028082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:56.538951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:03.701289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:16.421053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:22.938721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:29.506489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:36.497265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:43.920928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:50.561889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:57.786940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:04.896279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:12.729173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:19.247423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:25.980465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:32.715603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:41.698358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:49.602705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:56.920325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:04.108727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:16.893110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:23.299561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:30.138015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:36.886883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:44.369267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:50.965970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:58.325245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:05.288046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:13.068273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:19.677237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:26.432283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:33.204173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:42.198349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:50.080500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:57.341284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:04.561398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:17.295930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:23.683090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:30.593049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:37.286134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:44.756942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:51.517311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:58.778803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:05.701864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:13.446144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:20.086400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:26.838478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:33.734559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:42.804876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:50.557249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:57.799600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:04.987420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:17.701519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:24.128681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:31.037771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:37.652468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:45.224602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:52.020726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:59.235733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:06.619750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:13.859181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:20.515191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:27.236646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:34.237412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:43.251432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:51.002313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:58.266602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:05.411508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:18.120917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:24.535964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:31.470844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:38.057468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:45.654045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:52.474574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:59.681308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:07.055406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:14.301897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:20.947134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:27.624297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:35.645895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:43.688865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:51.485823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:58.667583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:05.930708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:18.561642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:24.918226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:32.103078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:38.485572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:46.040610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:52.887660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:00.157135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:07.485197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:14.663492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:21.404187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:28.092065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:36.110327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:44.167536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:51.968453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:59.067873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:06.374374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:18.980276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:25.307420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:32.521537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:38.981327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:46.446934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:53.328386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:00.582975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:07.891466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:15.118965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:21.822282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:28.565289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:36.553100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:44.662492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:52.415389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:59.493248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:06.803651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:19.354903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:25.730329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:32.892623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:39.407906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:46.851289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:53.736164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:00.985895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:08.367451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:15.509553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:22.242007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:29.031111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:37.063943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:45.098792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:52.836864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:59.861826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:07.201585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:19.749365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:26.088077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:33.279030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:40.243651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:47.226782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:54.103790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:01.385777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:08.803051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:15.851841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:22.598294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:29.404736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:37.507255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:45.520102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:53.223517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:00.242646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:07.630032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:20.176016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:26.463513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:33.701833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:40.885083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:47.610358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:54.623976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:01.802290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:09.259354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:16.198488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:22.958517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:29.793482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:38.322288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:45.969280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:53.650378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:00.709545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:08.041661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:20.593135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:26.832370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:34.089368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:41.332657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:47.977679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:55.098218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:02.225083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:09.670606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:16.579137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:23.374054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:30.169357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:38.766118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:46.459328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:54.147171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:01.148047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:08.548884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:20.963020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:27.229852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:34.510485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:41.794713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:48.398183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:55.530451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:02.669230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:10.128167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:17.230017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:23.911821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:30.647347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:39.236358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:46.960782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:54.627883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:01.583426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:08.991209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:21.329818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:27.638700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:34.917464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:42.237740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:48.846513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:56.106846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:03.125465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:11.086009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:17.674914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:24.361987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:31.088207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:39.727841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:47.428118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:55.112508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:01.964161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:09.421755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:21.778394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:28.097659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:35.338946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:42.681106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:49.294125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:56.571418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:03.579601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:11.515210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:18.054923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:24.768672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:31.490442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:40.335615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:47.875447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:55.567990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:02.398336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:09.887735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:22.131007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:28.447900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:35.694956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:43.086868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:49.683645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:46:56.962999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:04.045470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:11.912544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:18.393937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:25.132878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:31.868326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:40.775621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:48.313678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:47:55.991441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-06T15:48:02.781574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-06T15:48:36.153394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
account_age_daysagecredit_per_loancredit_scoredebt_to_incomedependents_to_incomeeducation_HSeducation_Masterseducation_Othereducation_PhDemployment_yearshas_credit_cardhome_ownership_OTHERhome_ownership_OWNhome_ownership_RENTincomeloan_amountloan_term_monthsloan_to_incomemarital_status_Marriedmarital_status_Singlemarital_status_Widowedmonthly_expensesnum_dependentsrecent_defaultregion_Northregion_Southregion_Westsavingssignup_dayofweeksin_agetarget_default_risk
account_age_days1.0000.007-0.014-0.0040.014-0.0160.0000.0000.0170.000-0.0030.0000.0000.0000.000-0.0020.015-0.0170.0160.0000.0000.0000.016-0.0200.0220.0090.0000.000-0.0120.005-0.0090.026
age0.0071.000-0.012-0.0000.0220.0140.0220.0180.0000.0310.0050.0190.0000.0030.008-0.0270.012-0.0040.0220.0000.0000.000-0.0070.0060.0250.0000.0000.0000.0080.005-0.1360.024
credit_per_loan-0.014-0.0121.0000.155-0.743-0.0020.0250.0000.0320.0120.0050.0000.0000.0000.012-0.003-0.9840.009-0.7620.0000.0240.000-0.010-0.0070.0000.0000.0000.016-0.0030.0210.0100.000
credit_score-0.004-0.0000.1551.000-0.0050.0060.0000.0090.0050.000-0.0170.0030.0170.0000.0180.0040.0030.005-0.0060.0250.0000.000-0.0000.0040.0000.0000.0190.0420.0100.009-0.0040.084
debt_to_income0.0140.022-0.743-0.0051.0000.2160.0000.0000.0080.013-0.0150.0000.0000.0000.000-0.5770.751-0.0170.9880.0000.0000.024-0.0010.0010.0000.0000.0000.0000.004-0.017-0.0140.495
dependents_to_income-0.0160.014-0.0020.0060.2161.0000.0000.0080.0280.045-0.0050.0210.0000.0000.000-0.3670.002-0.0080.2150.0000.0000.0210.0140.8800.0430.0160.0000.013-0.007-0.001-0.0090.370
education_HS0.0000.0220.0250.0000.0000.0001.0000.2880.1330.1280.0310.0090.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0100.0240.0000.007
education_Masters0.0000.0180.0000.0090.0000.0080.2881.0000.1120.1080.0170.0000.0000.0000.0000.0160.0030.0220.0000.0120.0000.0000.0000.0000.0000.0100.0000.0000.0000.0190.0110.000
education_Other0.0170.0000.0320.0050.0080.0280.1330.1121.0000.0480.0170.0010.0040.0000.0000.0300.0000.0000.0000.0000.0000.0020.0000.0130.0000.0000.0000.0000.0000.0080.0000.008
education_PhD0.0000.0310.0120.0000.0130.0450.1280.1080.0481.0000.0090.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0130.0280.000
employment_years-0.0030.0050.005-0.017-0.015-0.0050.0310.0170.0170.0091.0000.0160.0000.0000.0100.007-0.0090.013-0.0150.0000.0000.0000.004-0.0040.0000.0020.0290.028-0.0010.012-0.0290.025
has_credit_card0.0000.0190.0000.0030.0000.0210.0090.0000.0010.0000.0161.0000.0000.0020.0000.0240.0160.0000.0220.0000.0000.0060.0330.0140.0000.0230.0000.0080.0000.0260.0060.058
home_ownership_OTHER0.0000.0000.0000.0170.0000.0000.0000.0000.0040.0000.0000.0001.0000.1260.1970.0000.0140.0140.0220.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0140.0260.000
home_ownership_OWN0.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.1261.0000.5280.0030.0000.0110.0000.0080.0110.0000.0160.0000.0000.0000.0000.0120.0100.0000.0210.000
home_ownership_RENT0.0000.0080.0120.0180.0000.0000.0000.0000.0000.0000.0100.0000.1970.5281.0000.0000.0000.0130.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0230.022
income-0.002-0.027-0.0030.004-0.577-0.3670.0000.0160.0300.0000.0070.0240.0000.0030.0001.0000.0050.008-0.5740.0000.0000.000-0.0030.0070.0340.0000.0000.0000.0040.0050.0090.831
loan_amount0.0150.012-0.9840.0030.7510.0020.0000.0030.0000.000-0.0090.0160.0140.0000.0000.0051.000-0.0090.7700.0050.0300.0190.0090.0070.0000.0000.0130.0000.004-0.020-0.0110.000
loan_term_months-0.017-0.0040.0090.005-0.017-0.0080.0160.0220.0000.0000.0130.0000.0140.0110.0130.008-0.0091.000-0.0140.0170.0170.040-0.008-0.0010.0200.0310.0000.000-0.001-0.0050.0020.021
loan_to_income0.0160.022-0.762-0.0060.9880.2150.0000.0000.0000.023-0.0150.0220.0220.0000.000-0.5740.770-0.0141.0000.0000.0050.0180.0080.0020.0000.0000.0000.0170.001-0.017-0.0150.473
marital_status_Married0.0000.0000.0000.0250.0000.0000.0000.0120.0000.0000.0000.0000.0000.0080.0000.0000.0050.0170.0001.0000.7370.1890.0000.0200.0010.0200.0180.0000.0000.0000.0000.000
marital_status_Single0.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0300.0170.0050.7371.0000.2090.0000.0080.0000.0230.0110.0190.0000.0200.0000.000
marital_status_Widowed0.0000.0000.0000.0000.0240.0210.0000.0000.0020.0000.0000.0060.0000.0000.0000.0000.0190.0400.0180.1890.2091.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
monthly_expenses0.016-0.007-0.010-0.000-0.0010.0140.0000.0000.0000.0000.0040.0330.0260.0160.000-0.0030.009-0.0080.0080.0000.0000.0001.0000.0120.0330.0000.0000.010-0.0130.0030.0090.014
num_dependents-0.0200.006-0.0070.0040.0010.8800.0000.0000.0130.000-0.0040.0140.0000.0000.0040.0070.007-0.0010.0020.0200.0080.0000.0121.0000.0170.0000.0170.000-0.004-0.002-0.0070.000
recent_default0.0220.0250.0000.0000.0000.0430.0080.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0200.0000.0010.0000.0000.0330.0171.0000.0000.0000.0000.0000.0000.0130.000
region_North0.0090.0000.0000.0000.0000.0160.0000.0100.0000.0000.0020.0230.0000.0000.0000.0000.0000.0310.0000.0200.0230.0000.0000.0000.0001.0000.3330.3260.0200.0000.0000.000
region_South0.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0130.0000.0000.0180.0110.0000.0000.0170.0000.3331.0000.3300.0200.0000.0000.000
region_West0.0000.0000.0160.0420.0000.0130.0000.0000.0000.0080.0280.0080.0000.0120.0000.0000.0000.0000.0170.0000.0190.0000.0100.0000.0000.3260.3301.0000.0190.0170.0000.000
savings-0.0120.008-0.0030.0100.004-0.0070.0100.0000.0000.000-0.0010.0000.0000.0100.0000.0040.004-0.0010.0010.0000.0000.000-0.013-0.0040.0000.0200.0200.0191.000-0.009-0.0070.035
signup_dayofweek0.0050.0050.0210.009-0.017-0.0010.0240.0190.0080.0130.0120.0260.0140.0000.0000.005-0.020-0.005-0.0170.0000.0200.0000.003-0.0020.0000.0000.0000.017-0.0091.000-0.0040.000
sin_age-0.009-0.1360.010-0.004-0.014-0.0090.0000.0110.0000.028-0.0290.0060.0260.0210.0230.009-0.0110.002-0.0150.0000.0000.0000.009-0.0070.0130.0000.0000.000-0.007-0.0041.0000.000
target_default_risk0.0260.0240.0000.0840.4950.3700.0070.0000.0080.0000.0250.0580.0000.0000.0220.8310.0000.0210.4730.0000.0000.0000.0140.0000.0000.0000.0000.0000.0350.0000.0001.000

Missing values

2026-01-06T15:48:10.746607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-06T15:48:12.800656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageincomesavingsmonthly_expensesnum_dependentscredit_scoreloan_amountloan_term_monthsemployment_yearsrecent_defaulthas_credit_cardsignup_dayofweekdebt_to_incomesin_agetarget_default_riskloan_to_incomedependents_to_incomecredit_per_loanaccount_age_dayshome_ownership_OTHERhome_ownership_OWNhome_ownership_RENTeducation_HSeducation_Masterseducation_Othereducation_PhDmarital_status_Marriedmarital_status_Singlemarital_status_Widowedregion_Northregion_Southregion_West
030.066737.011155.02272.02.0605.07620426965.048.03.91.01.06.00.4040.14112010.4040430.0000300.0224382011FalseFalseTrueTrueFalseFalseFalseFalseTrueFalseFalseFalseTrue
122.070740.0997.01934.01.0683.2919674681.036.00.70.00.02.00.0660.80849610.0661710.0000140.1459402652FalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalse
268.038890.01929.01696.00.0658.00336012633.072.02.20.01.02.00.3250.49411300.3248310.0000000.0520822778FalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalse
349.029049.06284.02485.01.0707.47786420881.036.02.70.01.06.00.719-0.98245300.7187950.0000340.0338802816FalseTrueFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalse
474.060063.0924.03179.02.0564.76851119438.036.010.30.00.01.00.3240.89870810.3236210.0000330.0290532226FalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseTrue
556.037852.04826.03055.03.0686.86352915328.048.01.30.01.03.00.405-0.63126700.4049350.0000790.0448082616FalseFalseTrueFalseTrueFalseFalseFalseTrueFalseFalseTrueFalse
619.064635.05240.02737.02.0564.79994223469.048.06.70.01.01.00.3630.94630010.3630950.0000310.0240651484FalseFalseTrueTrueFalseFalseFalseFalseFalseFalseTrueFalseFalse
744.058003.06113.01607.01.0590.3098541000.048.00.50.00.02.00.017-0.95160210.0172400.0000170.5897202288FalseTrueFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalse
818.041132.014936.01414.02.0616.70103022800.072.02.10.01.02.00.5540.97384800.5542990.0000490.0270472820FalseTrueFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseFalse
929.051038.012639.02514.01.0604.9028076050.060.06.00.00.05.00.1190.23924900.1185370.0000200.0999671431FalseFalseTrueFalseTrueFalseFalseFalseTrueFalseFalseFalseFalse
ageincomesavingsmonthly_expensesnum_dependentscredit_scoreloan_amountloan_term_monthsemployment_yearsrecent_defaulthas_credit_cardsignup_dayofweekdebt_to_incomesin_agetarget_default_riskloan_to_incomedependents_to_incomecredit_per_loanaccount_age_dayshome_ownership_OTHERhome_ownership_OWNhome_ownership_RENTeducation_HSeducation_Masterseducation_Othereducation_PhDmarital_status_Marriedmarital_status_Singlemarital_status_Widowedregion_Northregion_Southregion_West
999059.032742.01186.01812.01.0631.21640034003.048.05.00.00.05.01.038-0.37387701.0384820.0000310.0185631095FalseFalseTrueTrueFalseFalseFalseFalseTrueFalseFalseFalseTrue
999125.046494.09050.02738.00.0682.63706320116.048.05.10.01.05.00.4330.59847210.4326490.0000000.0339332873FalseFalseTrueFalseTrueFalseFalseTrueFalseFalseFalseFalseTrue
999246.0100954.03499.01049.02.0590.86275811164.072.011.70.01.02.00.111-0.99369110.1105840.0000200.0529212141FalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrueFalse
999336.031124.02188.01427.01.0612.91832032198.036.00.30.01.06.01.034-0.44252001.0344740.0000320.019035989FalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalse
999442.061252.01911.01042.01.0774.95348513243.048.00.90.00.03.00.216-0.87157610.2162020.0000160.0585142763FalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalse
999554.044507.05975.02520.01.0699.63335231089.048.05.30.01.03.00.699-0.77276410.6985040.0000220.0225032140FalseFalseTrueTrueFalseFalseFalseFalseTrueFalseFalseFalseFalse
999650.020651.010203.01020.03.0680.7740668977.060.09.60.00.03.00.435-0.95892400.4346790.0001450.0758272693FalseFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalseFalse
999743.033827.03848.02562.01.0655.56274824319.060.04.30.00.04.00.719-0.91616600.7189020.0000300.0269562545TrueFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseTrue
999844.038273.015251.01060.02.0653.2776451000.024.011.40.01.06.00.026-0.95160200.0261270.0000520.6526252347FalseFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseFalse
999930.053614.06201.01310.01.0663.9755565205.060.09.80.01.05.00.0970.14112010.0970810.0000190.1275402866FalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse